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feat: remove object mode pipeline (YOLO, SigLIP, TRAINING_MODE, OBJECT_CLASS)

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winnow is a face recognition training tool. Object mode required manual
file placement with no Frigate API, pulled in torch/torchvision/transformers/
ultralytics (~2 GB), and was architecturally misaligned with the project goal.

Removed:
- process_object_mode (YOLO inference), get_yolo_model
- SigLIP model stack (get_siglip_model, get_object_embedding, batch variant)
- entity_type branching throughout diversity, embeddings, jobs, executor
- TRAINING_MODE and OBJECT_CLASS env vars
- torch, torchvision, transformers, ultralytics dependencies
- pytorch index entries from pyproject.toml
- Object mode from README (Modes section, env var table, How It Works)
This commit is contained in:
flan
2026-06-14 17:29:25 +00:00
parent 3105b15beb
commit 835016e0e3
7 changed files with 91 additions and 421 deletions
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winnow/jobs.py
+15 -44
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@@ -26,10 +26,8 @@ STRATEGY_PRESETS = {
}
def _get_strategy_choice(has_embedding: bool, entity_type: str) -> tuple[int | str, str]:
def _get_strategy_choice(has_embedding: bool) -> tuple[int | str, str]:
"""Prompt user for training strategy and return (limit, selection_mode)."""
model_name = "InsightFace" if entity_type == "face" else "SigLIP"
if has_embedding:
rprint(" [bold]1.[/bold] Adaptive Diversity [green][Recommended][/green]")
rprint(" [dim]• Dynamically selects images until redundancy starts[/dim]")
@@ -51,7 +49,7 @@ def _get_strategy_choice(has_embedding: bool, entity_type: str) -> tuple[int | s
return 30, "smart"
# Fallback when embedding model not available
rprint(f" [yellow]Note: {model_name} not available. Using Time Spread.[/yellow]")
rprint(" [yellow]Note: InsightFace not available. Using Time Spread.[/yellow]")
rprint(" [bold]1.[/bold] Standard (30 images) [green][Recommended][/green]")
rprint(" [bold]2.[/bold] Broad (100 images)")
rprint(" [bold]3.[/bold] Custom Count")
@@ -86,15 +84,13 @@ def _resolve_strategy(strategy: str, has_embedding: bool) -> tuple[int | str, st
def _perform_selection(
assets: list, limit: int | str, name: str, selection_mode: str, entity_type: str, person_id: str | None = None
assets: list, limit: int | str, name: str, selection_mode: str, person_id: str | None = None
) -> list:
"""Run diversity selection with progress display."""
if selection_mode == "smart":
model_display = "InsightFace (face embeddings)" if entity_type == "face" else "SigLIP (visual embeddings)"
rprint(f"\n[cyan]Using {model_display} for diversity analysis...[/cyan]")
rprint("\n[cyan]Using InsightFace (face embeddings) for diversity analysis...[/cyan]")
# Pre-load model explicitly (separate from availability check)
load_embedding_model(entity_type)
load_embedding_model()
with Progress(
SpinnerColumn(),
@@ -109,7 +105,6 @@ def _perform_selection(
limit,
name,
selection_mode=selection_mode,
entity_type=entity_type,
person_id=person_id,
progress_callback=lambda c, t: progress.update(task, completed=c, total=t),
)
@@ -120,9 +115,7 @@ def _perform_selection(
rprint(f"\n[cyan]Using time-spread selection for {limit} images...[/cyan]")
with console.status(f"[bold]Selecting {limit} images evenly distributed over time...[/bold]"):
selected = select_diverse_assets(
assets, limit, name, selection_mode="time", entity_type=entity_type, person_id=person_id
)
selected = select_diverse_assets(assets, limit, name, selection_mode="time", person_id=person_id)
rprint(f" [green]Selected {len(selected)} images using time spread.[/green]")
return selected
@@ -132,22 +125,12 @@ def _configure_person(person: dict, people: list[dict]) -> dict | None:
name = person["name"]
console.print(f"\nSelected: [bold green]{name}[/bold green]")
# Select training mode
rprint("\n[bold cyan]Training Mode:[/bold cyan]")
rprint(" [bold]1.[/bold] Face (Frigate Face Recognition)")
rprint(" [bold]2.[/bold] Object (Frigate Object Classification)")
mode_choice = Prompt.ask("Choice", choices=["1", "2"], default="1")
entity_type = "face" if mode_choice == "1" else "object"
config = {"name": name, "mode": entity_type, "quality_replacement": Config.QUALITY_REPLACEMENT}
if entity_type == "object":
config["object_class"] = Prompt.ask("Enter Object Class (e.g. dog, cat, car)", default="dog")
config = {"name": name, "mode": "face", "quality_replacement": Config.QUALITY_REPLACEMENT}
# Fetch and filter assets
years = IntPrompt.ask("Filter images older than (years)", default=Config.YEARS_FILTER)
console.print(f"Scanning for {name} ({entity_type})...")
console.print(f"Scanning for {name}...")
with console.status("[bold green]Fetching assets...[/bold green]"):
all_assets = fetch_all_assets(person)
recent_assets = filter_recent_assets(all_assets, years=years)
@@ -171,17 +154,15 @@ def _configure_person(person: dict, people: list[dict]) -> dict | None:
return None
# Strategy selection
has_embedding = is_embedding_available(entity_type)
has_embedding = is_embedding_available()
rprint(f"\n[bold cyan]Select Training Strategy for {name}:[/bold cyan]")
limit, selection_mode = _get_strategy_choice(has_embedding, entity_type)
limit, selection_mode = _get_strategy_choice(has_embedding)
if selection_mode == "skip":
return None
# Perform selection
selected_assets = _perform_selection(
recent_assets, limit, name, selection_mode, entity_type, person_id=person["id"]
)
selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, person_id=person["id"])
rprint(f" [green]Queued {len(selected_assets)} images for {name}.[/green]")
return {"person": person, "assets": selected_assets, "limit": len(selected_assets), "config": config}
@@ -231,7 +212,6 @@ def auto_configure(people: list[dict]) -> list[dict]:
rprint("[red]No people found with names in Immich.[/red]")
return []
mode = os.environ.get("TRAINING_MODE", "face")
strategy = os.environ.get("STRATEGY", "auto")
skip = os.environ.get("SKIP_PEOPLE", "").split(",") if os.environ.get("SKIP_PEOPLE") else []
only = os.environ.get("ONLY_PEOPLE", "").split(",") if os.environ.get("ONLY_PEOPLE") else []
@@ -260,11 +240,7 @@ def auto_configure(people: list[dict]) -> list[dict]:
jobs = []
for person in valid_people:
name = person["name"]
entity_type = mode
config = {"name": name, "mode": entity_type}
if entity_type == "object":
config["object_class"] = os.environ.get("OBJECT_CLASS", "dog")
config = {"name": name, "mode": "face"}
all_assets = fetch_all_assets(person)
recent_assets = filter_recent_assets(all_assets, years=Config.YEARS_FILTER)
@@ -307,7 +283,7 @@ def auto_configure(people: list[dict]) -> list[dict]:
config["quality_replacement"] = quality_replacement_only or Config.QUALITY_REPLACEMENT
has_embedding = is_embedding_available(entity_type)
has_embedding = is_embedding_available()
limit, selection_mode = _resolve_strategy(strategy, has_embedding)
# Cap selection to remaining capacity (no cap when replacement-only — executor
@@ -323,9 +299,7 @@ def auto_configure(people: list[dict]) -> list[dict]:
if selection_mode == "skip":
continue
selected_assets = _perform_selection(
recent_assets, limit, name, selection_mode, entity_type, person_id=person["id"]
)
selected_assets = _perform_selection(recent_assets, limit, name, selection_mode, person_id=person["id"])
if auto_cap is not None:
selected_assets = selected_assets[:auto_cap]
@@ -340,20 +314,17 @@ def _show_preview(jobs: list[dict]) -> None:
"""Show a summary table of all queued jobs before execution."""
table = Table(title="📋 Training Job Preview", show_header=True, header_style="bold cyan")
table.add_column("Person", style="bold")
table.add_column("Mode", style="dim")
table.add_column("Images", justify="right")
table.add_column("Date Range", style="dim")
for job in jobs:
name = job["person"]["name"]
mode = job["config"].get("mode", "face")
count = str(job["limit"])
# Date range
dates = sorted(a.get("fileCreatedAt", "")[:10] for a in job["assets"] if a.get("fileCreatedAt"))
date_range = f"{dates[0]} → {dates[-1]}" if len(dates) >= 2 else (dates[0] if dates else "—")
table.add_row(name, mode, count, date_range)
table.add_row(name, count, date_range)
console.print()
console.print(table)